code stringlengths 141 97.3k | apis sequencelengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
"""FastAPI app creation, logger configuration and main API routes."""
import logging
from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from injector import Injector
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks.global_handlers imp... | [
"llama_index.core.callbacks.CallbackManager",
"llama_index.core.callbacks.global_handlers.create_global_handler"
] | [((894, 921), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (911, 921), False, 'import logging\n'), ((1479, 1510), 'llama_index.core.callbacks.global_handlers.create_global_handler', 'create_global_handler', (['"""simple"""'], {}), "('simple')\n", (1500, 1510), False, 'from llama_index.c... |
import json
import os
from typing import Dict, List, Optional, Type
from loguru import logger
from datastore.datastore import DataStore
from models.models import (
DocumentChunk,
DocumentChunkMetadata,
DocumentChunkWithScore,
DocumentMetadataFilter,
Query,
QueryResult,
QueryWithEmbedding,
)
... | [
"llama_index.data_structs.struct_type.IndexStructType",
"llama_index.indices.query.schema.QueryBundle"
] | [((860, 929), 'os.environ.get', 'os.environ.get', (['"""LLAMA_INDEX_TYPE"""', 'IndexStructType.SIMPLE_DICT.value'], {}), "('LLAMA_INDEX_TYPE', IndexStructType.SIMPLE_DICT.value)\n", (874, 929), False, 'import os\n'), ((954, 999), 'os.environ.get', 'os.environ.get', (['"""LLAMA_INDEX_JSON_PATH"""', 'None'], {}), "('LLAM... |
import os
import weaviate
from llama_index.storage.storage_context import StorageContext
from llama_index.vector_stores import WeaviateVectorStore
from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
LLMPr... | [
"llama_index.llms.LocalAI",
"llama_index.LLMPredictor",
"llama_index.StorageContext.from_defaults",
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.embeddings.HuggingFaceEmbedding",
"llama_index.vector_stores.WeaviateVectorStore"
] | [((1360, 1409), 'llama_index.embeddings.HuggingFaceEmbedding', 'HuggingFaceEmbedding', ([], {'model_name': 'embed_model_name'}), '(model_name=embed_model_name)\n', (1380, 1409), False, 'from llama_index.embeddings import HuggingFaceEmbedding\n'), ((1418, 1534), 'llama_index.llms.LocalAI', 'LocalAI', ([], {'temperature'... |
import typer
import uuid
from typing import Optional, List, Any
import os
import numpy as np
from memgpt.utils import is_valid_url, printd
from memgpt.data_types import EmbeddingConfig
from memgpt.credentials import MemGPTCredentials
from memgpt.constants import MAX_EMBEDDING_DIM, EMBEDDING_TO_TOKENIZER_MAP, EMBEDDING... | [
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.embeddings.azure_openai.AzureOpenAIEmbedding",
"llama_index.core.Document",
"llama_index.embeddings.openai.OpenAIEmbedding"
] | [((981, 1020), 'llama_index.core.node_parser.SentenceSplitter', 'SentenceSplitter', ([], {'chunk_size': 'chunk_size'}), '(chunk_size=chunk_size)\n', (997, 1020), False, 'from llama_index.core.node_parser import SentenceSplitter\n'), ((5381, 5419), 'llama_index.embeddings.huggingface.HuggingFaceEmbedding', 'HuggingFaceE... |
import logging
import os
from typing import Optional
from typing import Type
import openai
from langchain.chat_models import ChatOpenAI
from llama_index import VectorStoreIndex, LLMPredictor, ServiceContext
from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters
from pydantic import BaseModel, Fi... | [
"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.ServiceContext.from_defaults"
] | [((754, 816), 'pydantic.Field', 'Field', (['...'], {'description': '"""the search query to search resources"""'}), "(..., description='the search query to search resources')\n", (759, 816), False, 'from pydantic import BaseModel, Field\n'), ((1840, 1905), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from... |
'''
Below helper functions are implemented in this script:
build_sentence_window_index - VectorStore Index for Sentence window RAG technique
get_sentence_window_query_engine - query enginer for the above index
build_automerging_index - VectorStore Index for Auto-merging RAG technique
get_automerging_query_engine - que... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.retrievers.AutoMergingRetriever",
"llama_index.node_parser.get_leaf_nodes",
"llama_index.ServiceContext.from_defaults",
"llama_index.StorageContext.from_defaults",
"llama_index.node_parser.SentenceWindowNodeParser.from_defaults",
"llama_index.V... | [((1446, 1596), 'llama_index.node_parser.SentenceWindowNodeParser.from_defaults', 'SentenceWindowNodeParser.from_defaults', ([], {'window_size': 'sentence_window_size', 'window_metadata_key': '"""window"""', 'original_text_metadata_key': '"""original_text"""'}), "(window_size=sentence_window_size,\n window_metadata_... |
from typing import Callable, List
def split_text_keep_separator(text: str, separator: str) -> List[str]:
"""Split text with separator and keep the separator at the end of each split."""
parts = text.split(separator)
result = [separator + s if i > 0 else s for i, s in enumerate(parts)]
return [s for s ... | [
"llama_index.utils.get_cache_dir"
] | [((876, 891), 'llama_index.utils.get_cache_dir', 'get_cache_dir', ([], {}), '()\n', (889, 891), False, 'from llama_index.utils import get_cache_dir\n'), ((912, 950), 'os.environ.get', 'os.environ.get', (['"""NLTK_DATA"""', 'cache_dir'], {}), "('NLTK_DATA', cache_dir)\n", (926, 950), False, 'import os\n'), ((1252, 1290)... |
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | [
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.readers.file.PyMuPDFReader",
"llama_index.core.schema.TextNode",
"llama_index.vector_stores.postgres.PGVectorStore.from_params",
"llama_index.core.query_engine.RetrieverQueryEngine.from... | [((1521, 1612), 'psycopg2.connect', 'psycopg2.connect', ([], {'dbname': '"""postgres"""', 'host': 'host', 'password': 'password', 'port': 'port', 'user': 'user'}), "(dbname='postgres', host=host, password=password, port=port,\n user=user)\n", (1537, 1612), False, 'import psycopg2\n'), ((1841, 1982), 'llama_index.vec... |
import os
import logging
import hashlib
import random
import uuid
import openai
from pathlib import Path
from llama_index import ServiceContext, GPTVectorStoreIndex, LLMPredictor, RssReader, SimpleDirectoryReader, StorageContext, load_index_from_storage
from llama_index.readers.schema.base import Document
from langcha... | [
"llama_index.GPTVectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.readers.schema.base.Document",
"llama_index.StorageContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.RssReader",
"llama_index.load_index_from_storage"
] | [((795, 827), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (809, 827), False, 'import os\n'), ((841, 869), 'os.environ.get', 'os.environ.get', (['"""SPEECH_KEY"""'], {}), "('SPEECH_KEY')\n", (855, 869), False, 'import os\n'), ((886, 917), 'os.environ.get', 'os.environ.get'... |
"""Configuration."""
import streamlit as st
import os
### DEFINE BUILDER_LLM #####
## Uncomment the LLM you want to use to construct the meta agent
## OpenAI
from llama_index.llms import OpenAI
# set OpenAI Key - use Streamlit secrets
os.environ["OPENAI_API_KEY"] = st.secrets.openai_key
# load LLM
BUILDER_LLM = Open... | [
"llama_index.llms.OpenAI"
] | [((316, 350), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-1106-preview"""'}), "(model='gpt-4-1106-preview')\n", (322, 350), False, 'from llama_index.llms import OpenAI\n')] |
from memgpt.data_types import Passage, Document, EmbeddingConfig, Source
from memgpt.utils import create_uuid_from_string
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.embeddings import embedding_model
from memgpt.data_types import Document, Passage
from typing import List, Iterator, D... | [
"llama_index.readers.web.SimpleWebPageReader",
"llama_index.core.node_parser.TokenTextSplitter",
"llama_index.core.Document"
] | [((1472, 1505), 'memgpt.embeddings.embedding_model', 'embedding_model', (['embedding_config'], {}), '(embedding_config)\n', (1487, 1505), False, 'from memgpt.embeddings import embedding_model\n'), ((5412, 5452), 'llama_index.core.node_parser.TokenTextSplitter', 'TokenTextSplitter', ([], {'chunk_size': 'chunk_size'}), '... |
from typing import Dict, List, Type
from llama_index.agent import OpenAIAgent, ReActAgent
from llama_index.agent.types import BaseAgent
from llama_index.llms import Anthropic, OpenAI
from llama_index.llms.llama_utils import messages_to_prompt
from llama_index.llms.llm import LLM
from llama_index.llms.replicate import ... | [
"llama_index.llms.OpenAI",
"llama_index.llms.Anthropic",
"llama_index.llms.replicate.Replicate"
] | [((1116, 1135), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': 'model'}), '(model=model)\n', (1122, 1135), False, 'from llama_index.llms import Anthropic, OpenAI\n'), ((1186, 1208), 'llama_index.llms.Anthropic', 'Anthropic', ([], {'model': 'model'}), '(model=model)\n', (1195, 1208), False, 'from llama_index.llms i... |
import asyncio
import os
import shutil
from argparse import ArgumentParser
from glob import iglob
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Union, cast
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
)
from llama_index.core.base.embeddings.base import Ba... | [
"llama_index.core.query_pipeline.query.QueryPipeline",
"llama_index.core.utils.get_cache_dir",
"llama_index.core.response_synthesizers.CompactAndRefine",
"llama_index.core.query_pipeline.components.function.FnComponent",
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.VectorStoreIndex.from_vec... | [((1789, 1840), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'description': '"""Query Pipeline to use for Q&A."""'}), "(description='Query Pipeline to use for Q&A.')\n", (1794, 1840), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field, validator\n'), ((2284, 2349), 'llama_index.core.bridg... |
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, List, Optional
if TYPE_CHECKING:
from llama_index.core.service_context import ServiceContext
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import BaseCallbackHandler, Callback... | [
"llama_index.core.embeddings.utils.resolve_embed_model",
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.core.callbacks.base.CallbackManager",
"llama_index.core.set_global_handler",
"llama_index.core.indices.prompt_helper.PromptHelper.from_llm_metadata",
"llama_index.core.utils.get_tokenizer... | [((1701, 1717), 'llama_index.core.llms.utils.resolve_llm', 'resolve_llm', (['llm'], {}), '(llm)\n', (1712, 1717), False, 'from llama_index.core.llms.utils import LLMType, resolve_llm\n'), ((2647, 2679), 'llama_index.core.embeddings.utils.resolve_embed_model', 'resolve_embed_model', (['embed_model'], {}), '(embed_model)... |
import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("CovidQaDataset", "./data")
... | [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
] | [((265, 315), 'llama_index.core.llama_dataset.download_llama_dataset', 'download_llama_dataset', (['"""CovidQaDataset"""', '"""./data"""'], {}), "('CovidQaDataset', './data')\n", (287, 315), False, 'from llama_index.core.llama_dataset import download_llama_dataset\n'), ((360, 412), 'llama_index.core.VectorStoreIndex.fr... |
from enum import Enum
from typing import Any, AsyncGenerator, Generator, Optional, Union, List
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
class MessageRole(str, Enum):
"""Message role."""
SYSTEM = "system"
... | [
"llama_index.core.bridge.pydantic.Field"
] | [((655, 682), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (660, 682), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((1146, 1172), 'llama_index.core.bridge.pydantic.Field', 'Field', ([], {'default_factory': 'str'}), '(def... |
"""Base agent type."""
import uuid
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.base.response.schema import RESPONSE_TYPE, Response
from lla... | [
"llama_index.core.bridge.pydantic.Field",
"llama_index.core.callbacks.trace_method"
] | [((1275, 1296), 'llama_index.core.callbacks.trace_method', 'trace_method', (['"""query"""'], {}), "('query')\n", (1287, 1296), False, 'from llama_index.core.callbacks import CallbackManager, trace_method\n'), ((1598, 1619), 'llama_index.core.callbacks.trace_method', 'trace_method', (['"""query"""'], {}), "('query')\n",... |
import json
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, Optional, Type
if TYPE_CHECKING:
from llama_index.core.bridge.langchain import StructuredTool, Tool
from deprecated import deprecated
from llama_index.core.bridge.pydantic import BaseModel
cl... | [
"llama_index.core.bridge.langchain.Tool.from_function",
"llama_index.core.bridge.langchain.StructuredTool.from_function"
] | [((1581, 1670), 'deprecated.deprecated', 'deprecated', (['"""Deprecated in favor of `to_openai_tool`, which should be used instead."""'], {}), "(\n 'Deprecated in favor of `to_openai_tool`, which should be used instead.')\n", (1591, 1670), False, 'from deprecated import deprecated\n'), ((1395, 1417), 'json.dumps', '... |
"""Generate SQL queries using LlamaIndex."""
import argparse
import json
import logging
import os
import re
from typing import Any, cast
from llama_index import LLMPredictor, SQLDatabase
from llama_index.indices import SQLStructStoreIndex
from llama_index.llms.openai import OpenAI
from sqlalchemy import create_engine,... | [
"llama_index.LLMPredictor",
"llama_index.SQLDatabase",
"llama_index.indices.SQLStructStoreIndex.from_documents",
"llama_index.llms.openai.OpenAI"
] | [((413, 431), 're.compile', 're.compile', (['"""\\\\s+"""'], {}), "('\\\\s+')\n", (423, 431), False, 'import re\n'), ((444, 462), 're.compile', 're.compile', (['"""\\\\n+"""'], {}), "('\\\\n+')\n", (454, 462), False, 'import re\n'), ((1926, 2003), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description... |
"""Utilities for Spider module."""
import json
import os
from typing import Dict, Tuple
from llama_index import LLMPredictor, SQLDatabase
from llama_index.indices import SQLStructStoreIndex
from llama_index.llms.openai import OpenAI
from sqlalchemy import create_engine, text
def load_examples(spider_dir: str) -> Tu... | [
"llama_index.indices.SQLStructStoreIndex",
"llama_index.SQLDatabase",
"llama_index.LLMPredictor"
] | [((1447, 1468), 'llama_index.LLMPredictor', 'LLMPredictor', ([], {'llm': 'llm'}), '(llm=llm)\n', (1459, 1468), False, 'from llama_index import LLMPredictor, SQLDatabase\n'), ((452, 464), 'json.load', 'json.load', (['f'], {}), '(f)\n', (461, 464), False, 'import json\n'), ((555, 567), 'json.load', 'json.load', (['f'], {... |
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